Name: Python Machine Learning Course, 1-Days, Online Attendance
Price: 185 GBP
Availability: InStock

<strong>In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.</strong>
        <p>Data preparation, data cleaning, data preprocessing (whatever you want to call it) is quite often the most tedious and time-consuming work in the data science/data analysis area. Especially if we are short of time and want to deliver crucial data analysis insights to our audience.

KNIME makes the data prep process efficient and easy. With KNIME, you can use the easy-to-use drag-and-drop interface, if you are not an experienced coder. But if you know how to work with languages such as R, Python, or Java, you can use them as well. This makes KNIME a truly flexible and versatile tool.

In this course, we will learn the efficient ways to import multiple files into KNIME, loops, web scraping, scripting (using Python code in KNIME), hyperparameter optimization, and feature selection. Also, learn basic machine learning workflows and helpful nodes for this in KNIME.

By the end of this course, you will be able to use KNIME for data cleaning and data preparation without any code.

All the resources and support files for this course are available at https://github.com/PacktPublishing/Data-science-and-Data-preparation-with-KNIME</p>
        <h2>What You Will Learn</h2><p>Enhance your basic KNIME skills already acquired<br>Increase your productivity and save time in your data preparation tasks<br>Discover what kind of loops are available and how to use them<br>Learn how to use Python in KNIME<br>Learn how to do data science in KNIME with and without coding<br>Learn basic machine learning workflows and helpful nodes</p>
        <h2>Audience</h2><p>This course is designed for aspiring data scientists and data analysts who want to work smarter, faster, and more efficiently. This course is also for anyone who wants to learn how to effectively clean data or encounter various data issues (for example, format) in the past and is looking for a solid solution, and who is familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course. Note: Tableau Desktop and Microsoft Power BI Desktop are optional.</p>
        <h2>Approach</h2><p>This course is practical and consists of a case study where you can and should follow along to solve tasks.</p>
        <h2>Key Features</h2><p>No coding required * Solve a data cleaning example together and enhance your basic KNIME skills * Increase your productivity and save time in your data preparation tasks</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-science-and-Data-preparation-with-KNIME</p>
        <h2>About the Author</h2><h3>Dan We</h3><p>Dan We is a 32-year-old entrepreneur, data scientist, and data analytics/visual analytics consultant. He holds a master's degree and is certified in Power BI as a qualified associate in Tableau. He is currently working in business intelligence and helps major companies get key insights from their data in order to deliver long-term growth and outpace their competitors.

He is committed to supporting other people by offering them educational services to help them accomplish their goals and become the best in their profession or explore a new career path.</p>
        <h2>Course Outline</h2><h4>1. Let's get into it! Data science and Data preparation with KNIME</h4><p></p><br><h4>2. Older videos KNIME version before 4.3</h4><p></p>

Packt

Data Science

In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.

Data science and Data preparation with KNIME

General Admission

<p>Transform your future with our stellar "Diploma in Python, Java, and Machine Learning" bundle, designed to ignite your passion for programming and data science. Dive deep into the complex world of Python and Java, two of the most versatile and widely-used programming languages of the 21st century, guided by our three QLS-endorsed courses. With this exclusive bundle, you will also receive a tangible recognition of your learning journey - hardcopy certificates that attest to your dedication and newfound skills.</p><p>Continue your thrilling journey of discovery with five additional courses, all CPD QS accredited, that expose you to the riveting realms of Azure Machine Learning, AWS SageMaker Canvas, and more. Grasp the nuances of Statistics and Probability specifically for Data Science, and master the A-Z of Data Science and Machine Learning with R, a powerful tool for statistical analysis and graphics.</p><p>Never has such a comprehensive package been offered. This course bundle not only imparts knowledge of leading programming languages but also goes beyond to equip you with a solid understanding of machine learning concepts and the exciting world of data science.</p><p>Whether you're looking to enhance your resume, break into a new field, or simply quench your thirst for knowledge, our eight-course bundle is the key to unlocking a world of potential. Get ready to redefine your learning experience and embark on an unforgettable journey of intellectual growth. Prepare to amaze yourself with what you can achieve!</p><p>Key Features of the Diploma in Python, Java and Machine Learning Bundle:</p><ul><li><strong>3 QLS-Endorsed Courses:</strong> We proudly offer 3 QLS-endorsed courses within our Diploma in Python, Java and Machine Learning bundle, providing you with industry-recognized qualifications. Plus, you'll receive a <strong>free hardcopy certificate</strong> for each of these courses.</li><ul><li><strong>QLS Course 01: Diploma in Python Programming</strong></li><li><strong> QLS Course 02: Introduction to Java</strong></li><li><strong> QLS Course 03: Machine Learning with Python</strong></li></ul></ul><ul><li><strong>5 CPD QS Accredited Courses</strong>: Additionally, our bundle includes 5 relevant CPD QS accredited courses, ensuring that you stay up-to-date with the latest industry standards and practices.</li><ul><li><strong>Course 01:</strong> Azure Machine Learning</li><li><strong>Course 02:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 03:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 04:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 05:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li></ul></ul><ul><li>In Addition, you'll get <strong>Five Career Boosting Courses</strong> absolutely FREE with this Bundle.</li><ul><li><strong>Course 01:</strong> Professional CV Writing</li><li><strong>Course 02:</strong> Job Search Skills</li><li><strong>Course 03:</strong> Self-Esteem &amp; Confidence Building</li><li><strong>Course 04:</strong> Professional Diploma in Stress Management</li><li><strong>Course 05:</strong> Complete Communication Skills Master Class</li></ul><li>Convenient Online Learning: Our Diploma in Python, Java and Machine Learning courses are accessible online, allowing you to learn at your own pace and from the comfort of your own home.</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Gain an in-depth understanding of Python and Java, two powerful programming languages that underpin modern computing.</li><li>Develop comprehensive knowledge of machine learning concepts, principles, and techniques using both Python and AWS SageMaker Canvas.</li><li>Understand how to implement and utilise Azure Machine Learning.</li><li>Grasp the fundamentals of Data Science, including crucial statistical methods and probability theory.</li><li>Learn the complete range of applications of R in Data Science and Machine Learning.</li><li>Build a strong theoretical foundation that enables critical thinking and problem-solving in real-world scenarios.</li><li>Achieve hardcopy certificates of QLS-endorsed courses as proof of your dedication and hard-earned skills.</li></ul> <p>Elevate your intellectual capabilities with our "Diploma in Python, Java, and Machine Learning" bundle. This bundle, featuring three QLS-endorsed and five CPD QS-accredited courses, is meticulously designed to bring you the best of programming languages, data science, and machine learning. It offers a deep dive into Python and Java, extends into the promising terrain of machine learning, and wraps up with a nuanced understanding of data science and related statistical methods. Unlock new doors of possibilities with this comprehensive and profound learning journey.</p> <h2>
        
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                            <ul><li>Individuals interested in learning programming languages such as Python and Java.</li><li>Enthusiasts wanting to gain a theoretical understanding of machine learning and data science.</li><li>Professionals seeking to broaden their knowledge base and enhance their resumes.</li><li>Anyone with a passion for lifelong learning and a thirst for knowledge in tech-related fields.</li></ul>
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                            <ul><li><strong>Python Developer:</strong> £30,000 - £70,000 per year. (Average Salary range in the UK)</li><li><strong>Java Developer:</strong> £70,000 per year.</li><li><strong>Machine Learning Engineer:</strong> £40,000 - £80,000 per year.</li><li><strong>Data Scientist:</strong> £35,000 - £80,000 per year.</li><li><strong>Azure/AWS Machine Learning Specialist:</strong> £40,000 - £90,000 per year.</li><li><strong>Statistician: </strong>£30,000 - £60,000 per year.</li></ul>
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        <h4> Hard copy certificate</h4>
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Academy for Health and Fitness

Learning & Development

Managed Learning Programme (MLP)

Diploma in Professional Studies (DPS)

ICDL (International Certification of Digital Literacy)

Diploma in Python, Java and Machine Learning

<p>Imagine yourself effortlessly coding and creating groundbreaking applications that will revolutionise how people interact with technology. With our bundle of courses, you'll be able to master three of the most in-demand programming languages in the tech industry: Python, Java, and Machine Learning.</p> <p>With our Diploma in Python Programming, you'll gain wisdom in Python's syntax, data structures, and algorithms. This course will equip you with the ability to write efficient, reliable, and scalable code that will help you easily build complex applications.</p> <p>Once you've mastered Python, you'll move on to Introduction to Java, where you'll learn about the fundamental concepts of Java programming language, including object-oriented programming, Java classes, and methods. This course will give you the talent to create powerful Java applications that can run on any device.</p> <p>Finally, with Machine Learning with Python, you'll learn how to use Python to build intelligent machines that can learn from data. This course will provide the tools and techniques you need to develop and deploy machine-learning models in real-world applications.</p> <p>We recognise the distinctiveness and obstacles that each community faces. We developed our courses to be particularly personalised and matched to your requirements because of this. Our classes are intended to be engaging and simple to comprehend, ensuring that you remember the wisdom you need to contribute to your community.</p> <p>This bundle demonstrates the excellent level of education obtained through QLS-endorsed courses. This bundle includes three courses, ensuring learners receive high-quality, instructive, enjoyable learning.</p> <p>Investing in your education is always a sensible decision, and with 3 QLS Endorsed certifications under your belt, you can be confident that you're double-boosting your rÃ©sumÃ©. Besides the QLS courses, the individuals will have access to 10 CPD courses related to the subject. They will widen your wisdom and improve your general comprehension. So why wait? Sign up today and start your journey to becoming a programming mastermind!</p> <p><strong>QLS Endorsed Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Diploma in Python Programming at QLS Level 5</li><li><strong>Course 02: </strong>Diploma in Introduction to Java at QLS Level 5</li><li><strong>Course 03</strong>: Certificate in Machine Learning with Python at QLS Level 3</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 04:</strong> Azure Machine Learning</li><li><strong>Course 05:</strong> Data Science &amp; Machine Learning with Python</li><li><strong>Course 06:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 07:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 08:</strong> Data Science &amp; Machine Learning with R</li></ul> <p>Take your career to the next level with our bundle that includes not only technical courses but also <strong>five guided courses</strong> focused on personal development and career growth.</p> <ul> <li><strong>Course 09: </strong>Personal Development</li><li><strong>Course 10:</strong> Career Development Plan Fundamentals</li><li><strong>Course 11: </strong>Leadership and Management Course</li><li><strong>Course 12: </strong>Communication Building Masterclass</li><li><strong>Course 13: </strong>Positive Psychology Masterclass</li></ul> <p>Learning Outcomes:</p> <ul> <li>Realise the basic principles of programming languages such as Python and Java</li><li>Learn about the different data types, control structures, and functions in Python and Java</li><li>Expand wisdom in writing, testing, and debugging code using Python and Java</li><li>Understand the principles of machine learning and its applications</li><li>Learn about the different types of machine learning algorithms and their implementation using Python</li><li>Develop an understanding of data analysis, data visualisation, and machine learning techniques</li><li>Comprehend the principles of software development and its practices</li><li>Learn about the different stages of the software development lifecycle and their implementation</li></ul> <p><strong>Please Note: </strong>Imperial Academy is One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p>This course bundle provides a comprehensive overview of three essential aspects of programming: Python programming, Java programming, and machine learning with Python. The courses are designed to equip learners with the knowledge and skills necessary to excel in programming and data science.</p> <p><strong>The Diploma in Python Programming course</strong> covers the fundamental principles of programming with Python, including data types, control structures, and functions. Learners will gain a thorough understanding of Python syntax and learn how to develop programs that solve real-world problems.</p> <p><strong>The Introduction to Java</strong> course provides learners with an understanding of the key principles of programming with Java, including object-oriented programming concepts and syntax. They will learn how to develop programs using Java and create user interfaces using graphical user interface (GUI) components.</p> <p><strong>The Machine Learning with Python</strong> course covers machine learning principles, including data preprocessing, model selection, and evaluation. Learners will gain a thorough understanding of machine learning algorithms and learn how to apply these algorithms to solve real-world problems using Python.</p> <p><strong>Certificate</strong></p> <p><strong>QLS-endorsed</strong> courses are designed to provide learners with the skills and knowledge they need to succeed in their chosen field.</p> <p>The Quality Licence Scheme is a distinguished and respected accreditation in the UK, denoting exceptional quality and excellence. It carries significant weight among industry professionals and recruiters.</p> <p>Upon completion, learners will receive Three <strong>QLS Endorsed</strong> hard copy certificates, making it easy for them to showcase their achievements. These certificates validate their course completion, the level of the course they have completed, and the <strong>QLS endorsement.</strong></p><h2>
        
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                             <ul> <li>Students or professionals in computer science, software engineering, or related fields</li><li>Programmers or developers looking to expand their knowledge in Python, Java, or machine learning</li><li>Entrepreneurs looking to start a software development or machine learning business</li><li>Anyone interested in learning about data analysis, data visualisation, or machine learning techniques</li><li>Those who want to learn about the principles of software development and its practices</li><li>Individuals interested in pursuing a career in artificial intelligence, data science, or software engineering</li></ul> 
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                             <p>Career Path:</p> <ul> <li>Python Developer - £25,000-£60,000</li><li>Java Developer - £25,000-£60,000</li><li>Machine Learning Engineer - £30,000-£70,000</li><li>Data Analyst - £20,000-£45,000</li><li>Software Engineer - £25,000-£60,000</li><li>Full-stack Developer - £25,000-£60,000</li><li>Artificial Intelligence Developer - £30,000-£70,000</li></ul> 
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                                    <h4>Certificate of completion</h4>
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Imperial Academy

3 QLS Endorsed Diploma | QLS Hard Copy Certificate Included | 10 CPD Courses | Lifetime Access | 24/7 Tutor Support

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					<div><p>The comprehensive Microsoft Azure Machine Learning has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Microsoft Azure Machine Learning today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Microsoft Azure Machine Learning has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Microsoft Azure Machine Learning is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Microsoft Azure Machine Learning is CPD-accredited, so you can be confident you're completing a quality training course  will boost your CV and enhance your career potential. The Microsoft Azure Machine Learning is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Microsoft Azure Machine Learning, you will be awarded a certificate of completion  as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Microsoft Azure Machine Learning will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Microsoft Azure Machine Learning to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Microsoft Azure Machine Learning

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			<h4>Overview</h4>		</div>
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							<p><span>By enroling in Machine Learning Basics, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.</span></p><p><span>The Machine Learning Basics course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent Machine Learning Basics course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our Machine Learning Basics Program.</span></p><p><span>So enrol in our Machine Learning Basics course right away if you're keen to envision yourself in a rewarding career.</span></p>						</div>
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							<p><span>Enroling in this Machine Learning Basics course can improve your Machine Learning Basics perspective, regardless of your skill levels in the Machine Learning Basics topics you want to master. If you're already a Machine Learning Basics expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced Machine Learning Basics insights that will help you make the most of your time. This Machine Learning Basics course will act as a guide for you if you've ever wished to excel at Machine Learning Basics.</span></p>						</div>
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										<span>Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.</span>
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							<p>Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.</p>						</div>
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							<p><span>This Machine Learning Basics course is a great place to start if you're looking to start a new career in Machine Learning Basics field. This training is for anyone interested in gaining in-demand Machine Learning Basics proficiency to help launch a career or their business aptitude. </span></p>						</div>
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							<p><span>The Machine Learning Basics course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Machine Learning Basics, without any fuss.</span></p>						</div>
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							<p><span>This Machine Learning Basics training will assist you develop your Machine Learning Basics ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a  Machine Learning Basics professional story and personalise your path to a new career. Furthermore, developing this Machine Learning Basics skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.</span></p>						</div>
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			<h4>Order Your Certificate</h4>		</div>
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							<div><div><div><div id="1693981461.590199"><div><div><div><div><div><div><div><div><div><div><div>To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:</div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>						</div>
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				<h3>
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					<td colspan="4"> Section 01: Introduction</td>
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					<td>Introduction to Supervised Machine Learning</td>
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					<td><span><i></i> 00:06:00</span></td>
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					<td colspan="4">Section 02: Regression</td>
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					<td>Introduction to Regression</td>
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					<td><span><i></i> 00:13:00</span></td>
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					<td>Evaluating Regression Models</td>
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					<td><span><i></i> 00:11:00</span></td>
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					<td>Conditions for Using Regression Models in ML versus in Classical Statistics</td>
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					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Statistically Significant Predictors</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Models Including Categorical Predictors. Additive Effects</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Models Including Categorical Predictors. Interaction Effects</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 03: Predictors</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Multicollinearity among Predictors and its Consequences</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Prediction for New Observation Confidence Interval and Prediction Interval</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Model Building. What if the Regression Equation Contains 'Wrong' Predictors?</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 04: Minitab</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Stepwise Regression and its Use for Finding the Optimal Model in Minitab</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression with Minitab. Example. Auto-mpg: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression with Minitab. Example. Auto-mpg: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 05: Regression Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Basic idea of Regression Trees</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees with Minitab. Example. Bike Sharing: Part1</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees with Minitab. Example. Bike Sharing: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 06: Binary Logistics Regression</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Binary Logistics Regression</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 07: Classification Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Classification Trees</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Node Splitting Methods 1. Splitting by Misclassification Rate</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Node Splitting Methods 2. Splitting by Gini Impurity or Entropy</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicted Class for a Node</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Goodness of the Model - 1. Model Misclassification Cost</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predefined Prior Probabilities and Input Misclassification Costs</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Building the Tree</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classification Trees with Minitab. Example. Maintenance of Machines: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classification Trees with Miitab. Example. Maintenance of Machines: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 08: Data Cleaning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Cleaning: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Cleaning: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating New Features</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 09: Data Models</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Polynomial Regression Models for Quantitative Predictor Variables</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Interactions Regression Models for Quantitative Predictor Variables</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Qualitative and Quantitative Predictors: Interaction Models</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Final Models for Duration and Total Charge: Without Validation</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Underfitting or Overfitting: The 'Just Right Model'</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for Duration</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for Duration: A More Detailed Error Analysis</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for TotalCharge</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for ToralCharge: A More Detailed Error Analysis</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 10: Learning Success</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees for Duration and TotalCharge</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: The Problem Statement</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: Binary Logistic Regression Models</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: Classification Tree Models</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Order Your Certificate</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Order Your Certificate</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

NextGen Learning

Machine Learning Basics Course

Analytics

Python

This course covers Python for data science and machine learning in detail and is for a beginner in Python. You will also learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, challenges of bias, variance and overfitting, model evaluation techniques, model optimization using hyperparameter tuning, grid search cross-validation techniques, and more.

Practical Data Science Using Python.

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks.</p>Overview<p>After completing this course, you will be able to:<br />Provision an Azure Databricks workspace and cluster<br />Use Azure Databricks to train a machine learning model<br />Use MLflow to track experiments and manage machine learning models<br />Integrate Azure Databricks with Azure Machine Learning</p><p>Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this course, students will learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.<br /></p></div><div id="course-pills-2"><h4>Introduction to Azure Databricks</h4><ul><li>Getting Started with Azure Databricks</li><li>Working with Data in Azure Databricks</li></ul><h4>Training and Evaluating Machine Learning Models</h4><ul><li>Preparing Data for Machine Learning</li><li>Training a Machine Learning Model</li></ul><h4>Managing Experiments and Models</h4><ul><li>Using MLflow to Track Experiments</li><li>Managing Models</li></ul><h4>Managing Experiments and Models</h4><ul><li>Using MLflow to Track Experiments</li><li>Managing Models</li></ul><h4>Integrating Azure Databricks and Azure Machine Learning</h4><ul><li>Tracking Experiments with Azure Machine Learning</li><li>Deploying Models</li></ul></div>

Nexus Human

Microsoft AZ-900

Microsoft AZ-104

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-090T00: Implementing a Machine Learning Solution with Microsoft Azure Databricks

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Did you know that Python is one of the most popular programming languages in the world, with a massive 8.2 million users? It&#39;s no wonder the demand for skilled Python programmers is increasing daily. If you&#39;re looking to learn Python and launch a career as a programmer, look no further than our <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programmer Complete Bundle - QLS Endorsed.</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Our bundle includes 10 comprehensive courses designed to give learners the aptitudes and understanding they need to become successful Python programmers. From Python programming for non-programmers to data science, machine learning, and web development, our courses cover everything you need to know to become a skilled Python programmer.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Why do people in the UK learn Python programming? Here&#39;s a startling statistic: the demand for Python programming skills has increased by 22.5% in the UK in the last year alone, with an average salary of &pound;45,000 per annum. By taking our Python Programmer Complete Bundle, learners can gain the skills and knowledge they need to take advantage of this growing demand and launch a successful career in programming.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">And the best part? Our bundle is endorsed by the Quality Licence Scheme, ensuring the highest quality of education. Upon completion of the courses, learners will receive <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">10 QLS Endorsed Certificates in Hard Copy, free of charge.</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Take your first step towards a successful career in programming today with our <strong id="isPasted" fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programmer Complete Bundle - QLS Endorsed</strong> and become a proficient Python programmer in no time.<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></strong></p><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This bundle comprises the following courses:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">QLS Endorsed Courses:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 01:</strong> Python Programming for Non-Programmers Level 5</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 02:</strong> Python Data Science for Beginners</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 03:</strong> Machine Learning with Python</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 04:</strong> Azure Machine Learning</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 05:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 06:</strong> Introduction to Java</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 07:</strong> HTML Web Development Crash Course</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 08:</strong> How To Build Easy Drag &amp; Drop Landing Pages With WordPress</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 09:</strong> Career Coaching</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 10:</strong> The Unstoppable Self-Confidence &amp; High Self-Esteem Blueprint</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcomes:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Understand the fundamentals of Python programming</li><li fr-original-style="" style="box-sizing: border-box;">Use Python for data science and analysis</li><li fr-original-style="" style="box-sizing: border-box;">Apply machine learning techniques using Python</li><li fr-original-style="" style="box-sizing: border-box;">Understand the basics of Azure Machine Learning</li><li fr-original-style="" style="box-sizing: border-box;">Understand the basics of statistics and probability for data science and machine learning</li><li fr-original-style="" style="box-sizing: border-box;">Create web pages using HTML and WordPress</li><li fr-original-style="" style="box-sizing: border-box;">Develop the aptitudes and confidence needed to pursue a successful career in programming</li></ul><p dir="ltr" id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Why Choose Imperial Academy?</strong></span></p><ul fr-original-style="" style="box-sizing: border-box;"><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">Fully Online Training</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">QLS Endorsed &amp; CPD Accredited Certificate</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">No Time Limits. Study at Your Own Pace</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">Expert Instructors</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">Audio Visual Training</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">Up-to-date Curriculum and Valuable Guidelines</span></li></ul><h2 style="color: rgb(65, 65, 65); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(65, 65, 65);">Course&nbsp;Description</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Our <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programmer Complete Bundle - QLS Endorsed</strong> is a comprehensive package of ten courses designed to provide learners with the knowledge needed to become successful Python programmers. Our courses cover a wide range of topics, including Python programming for non-programmers, data science, machine learning, and web development.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Whether you&#39;re an aspiring programmer or a business owner looking to develop your website, our Python Programmer Complete Bundle has something for you. By completing our courses, learners will gain the skills and confidence needed to pursue a successful career in programming and data analysis. With our <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">QLS Endorsed Hard Copy Certificates,</strong> learners can be confident in the quality of their education and prepared to take advantage of the growing demand for Python programming skills.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Certification:&nbsp;</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Quality Licence Scheme ensures excellence and quality. These courses&#39; learning outcomes also provide the learners with high competence in their line of work. So,<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> QLS Endorsed</strong> courses deliver deep knowledge and add significant value to the resume.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">After completing the assessments, learners will be eligible for a prestigious <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">QLS Certificate</strong> for each endorsed course in this bundle. Moreover, the certificates will be delivered to their home free of cost!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Imperial Academy is an approved reselling partner for Quality Licence Scheme courses under One Education.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2><div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Individuals interested in a career in programming and data analysis</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to expand their knowledge of programming and data analysis</li><li fr-original-style="" style="box-sizing: border-box;">Business owners seeking to develop their own websites and landing pages</li><li fr-original-style="" style="box-sizing: border-box;">Students seeking to gain the talents and expertise to pursue a career in programming and data analysis</li><li fr-original-style="" style="box-sizing: border-box;">Anyone interested in developing the ability and confidence needed to succeed in the rapidly growing programming and data analysis field.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Requirements</div>
    </h2><div id="requirements" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Anyone can enrol in this <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programmer Complete Bundle - QLS Endorsed</strong>.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2><div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Python Developer: &pound;35,000 - &pound;75,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Data Analyst: &pound;25,000 - &pound;45,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Machine Learning Engineer: &pound;50,000 - &pound;100,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Web Developer: &pound;25,000 - &pound;45,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Software Engineer: &pound;30,000 - &pound;60,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Business Intelligence Analyst: &pound;30,000 - &pound;65,000 per annum</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2><div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">Hard Copy QLS Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Hard copy certificate -
            Included
        </p>
        <h4 fr-original-style="" style="box-sizing: border-box;">PDF Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
        <h4 fr-original-style="" style="box-sizing: border-box;">Student ID Card</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>

                        </div>

Machine Learning (ML)

Python Programmer Complete Bundle - QLS Endorsed

<strong>This course is a perfect supplement for ML enthusiasts. If you are only just beginning your adventures in machine learning and want to know the basics of statistics and regression used for machine learning, then go for it. Discover how you can level up and gain confidence to implement statistical methods and regression in machine learning with Python.</strong>
        <p>This course is for ML enthusiasts who want to understand basic statistics and regression for machine learning. The course starts with setting up the environment and understanding the basics of Python language and different libraries. Next, you'll see the basics of machine learning and different types of data. After that, you'll learn a statistics technique called Central Tendency Analysis.

Post this, you'll focus on statistical techniques such as variance and standard deviation. Several techniques and mathematical concepts such as percentile, normal distribution, uniform distribution, finding z-score, linear regression, polynomial linear regression, and multiple regression with the help of manual calculation and Python functions are introduced as the course progresses.

The dataset will get more complex as you proceed ahead; you'll use a CSV file to save the dataset. You'll see the traditional and complex method of finding the coefficient of regression and then explore ways to solve it easily with some Python functions.

Finally, you'll learn a technique called data normalization or standardization, which will improve the performance of the algorithms very much compared to a non-scaled dataset.

By the end of this course, you'll gain a solid foundation in machine learning and statistical regression using Python.

All the code files and related files are available on the GitHub repository at https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python</p>
        <h2>What You Will Learn</h2><p>Set up the environment<br>Learn central tendency analysis<br>Learn statistical models and analysis<br>Learn regression models and analysis<br>Use NumPy, matplotlib, and scikit-learn libraries<br>Learn the data normalization or standardization technique</p>
        <h2>Audience</h2><p>This course is for beginners and individuals who want to learn mathematics for machine learning. You need not have any prior experience or knowledge in coding; just be ready with your learning mindset at the highest level.<br><br>Individuals interested in learning what's actually happening behind the scenes of Python functions and algorithms (at least in a shallow layman's way) will be highly benefitted.<br><br>Basic computer knowledge and an interest to learn mathematics for machine learning is the only prerequisite for this course.</p>
        <h2>Approach</h2><p>This is a comprehensive and hands-on course to learn from basic to advanced mathematics and statistical concepts that cover machine learning algorithms. The instructor will take you through every step of the code.<br><br>The instructor shows both the manual calculation approach and then the Python functions to work around in solving statistical and regression problems.</p>
        <h2>Key Features</h2><p>A comprehensive course that includes Python coding, visualization, loops, variables, and functions * Manual calculation and then using Python functions/codes to understand the difference * Beginner to advanced mathematics and statistical concepts that cover machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python</p>
        <h2>About the Author</h2><h3>Abhilash Nelson</h3><p>Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.</p>
        <h2>Course Outline</h2><h4>1. Course Introduction and Table of Contents</h4><p></p><br><h4>2. Environment Setup: Preparing  your Computer</h4><p></p><br><h4>3. Essential Components Included in Anaconda</h4><p></p><br><h4>4. Python Basics - Assignment</h4><p></p><br><h4>5. Python Basics - Flow Control</h4><p></p><br><h4>6. Python Basics - List and Tuples</h4><p></p><br><h4>7. Python Basics - Dictionary and Functions</h4><p></p><br><h4>8. Numpy Basics</h4><p></p><br><h4>9. Matplotlib Basics</h4><p></p><br><h4>10. Basics of Data for Machine Learning</h4><p></p><br><h4>11. Central Data Tendency - Mean</h4><p></p><br><h4>12. Central Data Tendency - Median and Mode</h4><p></p><br><h4>13. Variance and Standard Deviation Manual Calculation</h4><p></p><br><h4>14. Variance and Standard Deviation using Python</h4><p></p><br><h4>15. Percentile Manual Calculation</h4><p></p><br><h4>16. Percentile using Python</h4><p></p><br><h4>17. Uniform Distribution</h4><p></p><br><h4>18. Normal Distribution</h4><p></p><br><h4>19. Manual Z score calculation</h4><p></p><br><h4>20. Z score calculation using python</h4><p></p><br><h4>21. Multi Variable Dataset Scatter Plot</h4><p></p><br><h4>22. Introduction to Linear Regression</h4><p></p><br><h4>23. Manually finding Linear Regression Correlation Coefficient</h4><p></p><br><h4>24. Manually finding Linear Regression Slope Equation</h4><p></p><br><h4>25. Manually Predicting the Future Value using Equation</h4><p></p><br><h4>26. Linear Regression using Python Introduction</h4><p></p><br><h4>27. Linear Regression using Python</h4><p></p><br><h4>28. Strong and Weak Linear Regression</h4><p></p><br><h4>29. Predicting Future value using Linear Regression in Python</h4><p></p><br><h4>30. Polynomial Regression Introduction</h4><p></p><br><h4>31. Polynomial Regression Visualization</h4><p></p><br><h4>32. Polynomial Regression Prediction and R2 value</h4><p></p><br><h4>33. Polynomial Regression Finding SD Components</h4><p></p><br><h4>34. Polynomial Regression Manual Method Equations</h4><p></p><br><h4>35. Finding SD components for abc</h4><p></p><br><h4>36. Finding abc</h4><p></p><br><h4>37. Polynomial Regression Equation and Prediction</h4><p></p><br><h4>38. Polynomial Regression coefficient</h4><p></p><br><h4>39. Multiple Regression Introduction</h4><p></p><br><h4>40. Multiple Regression using Python - Part 1 - Data Import as CSV</h4><p></p><br><h4>41. Multiple Regression using Python - Part 2 - Data Visualization</h4><p></p><br><h4>42. Creating Multiple Regression Object and Prediction using Python</h4><p></p><br><h4>43. Manual Multiple Regression - Intro and Finding Means</h4><p></p><br><h4>44. Manual Multiple Regression - Finding Components</h4><p></p><br><h4>45. Manual Multiple Regression - Finding a b c</h4><p></p><br><h4>46. Manual Multiple Regression Equation Prediction and Coefficients</h4><p></p><br><h4>47. Feature Scaling Introduction</h4><p></p><br><h4>48. Standardization Scaling using Python</h4><p></p><br><h4>49. Standardization Scaling using Manual Calculation</h4><p></p><br><h4>50. Further Learning References and Resource Download</h4><p></p>

Statistics

Programming

This course is a perfect supplement for ML enthusiasts. If you are only just beginning your adventures in machine learning and want to know the basics of statistics and regression used for machine learning, then go for it. Discover how you can level up and gain confidence to implement statistical methods and regression in machine learning with Python.

Basic Statistics and Regression for Machine Learning in Python

London

<div style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms.</strong></p></div><div data-testid="organizerBrief" fr-original-style="" style="box-sizing: border-box;"><div data-testid="creator-signal-main-section" fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"></section></div></div><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></strong></p><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Machine Learning 2-day Course</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Prerequisites:</strong></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Basic knowledge of Python coding is a pre-requisite.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Who Should Attend?</strong></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Practical:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project</li><li fr-original-style="" style="box-sizing: border-box;">We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.&nbsp;</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course Outline:</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Supervised Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine</li><li fr-original-style="" style="box-sizing: border-box;">Regression Algorithms: Linear, Polynomial</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Unsupervised Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Clustering Algorithms: K-means clustering, Hierarchical Clustering</li><li fr-original-style="" style="box-sizing: border-box;">Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)</li><li fr-original-style="" style="box-sizing: border-box;">Association Machine Learning Algorithms: Apriori, Euclat</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Other machine learning Algorithms:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting</li><li fr-original-style="" style="box-sizing: border-box;">Reinforcement learning Algorithms: Q-Learning</li><li fr-original-style="" style="box-sizing: border-box;">Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Exploration and Preprocessing:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">The first part of a Machine Learning project understands the data and the problem at hand.</li><li fr-original-style="" style="box-sizing: border-box;">Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is included in this Python Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Python Machine Learning Certificate on completion &nbsp;</li><li fr-original-style="" style="box-sizing: border-box;">Python Machine Learning notes</li><li fr-original-style="" style="box-sizing: border-box;">Practical Python Machine Learning exercises and code examples</li><li fr-original-style="" style="box-sizing: border-box;">After the course, 1 free, online session for questions or revision Python Machine Learning.</li><li fr-original-style="" style="box-sizing: border-box;">Max group size on this Python Machine Learning is 4.</li></ul></div><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p><div style="color: rgb(30, 10, 60); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(30, 10, 60);"><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h2 fr-original-style="" style="box-sizing: border-box;">Refund Policy</h2></div><div style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">No Refunds</div>

Pcw Courses Ltd

Python Machine Learning Course, 1-Days, Online Attendance

Python Basics 1-days Online Instructor-Led

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Data Engineering

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

TensorFlow

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Learning To Learn

Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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